WaveMaker predicts AI shift in code, UIs & agents by 2026

Software makers face a growing divide between rapid AI-driven prototyping and the slower realities of production deployment, according to new commentary from WaveMaker's Head of AI Product Engineering, who expects 2026 to bring sharper focus on code quality, architectural oversight and AI-driven user interface personalisation.

Prashant Reddy, Head of AI Product Engineering at low-code platform provider WaveMaker, set out a series of predictions on how generative AI will reshape development teams, enterprise applications and product strategy over the next year.

He argued that the ability to generate code faster does not remove longstanding constraints in testing, governance and system design.

Code and oversight

Reddy said that code generation with AI tools changes where value sits in the software lifecycle. Human judgement and senior engineering expertise become more central as AI output scales.

"Generating code quickly does not equal getting to deploy to production quickly. With AI generating code, the focus shifts to people who can review the code for quality and provide architectural oversight. There are reports that senior developers are more productive when they use AI because they have experience, and this is because they know how to evaluate, review, and accept the code thrown at them. Generating code quickly is a value at the prototyping phase - or in the sphere of ideation, where you don't exactly know what to do, or how to shape your product. Here, a prototype application unlocks higher bandwidth in communicating intent, direction for the rest of team members, and a future of AI tools ahead," said Prashant Reddy, Head of AI Product Engineering, WaveMaker.

Many software teams now use generative AI assistants inside integrated development environments. These tools can propose functions, tests and boilerplate code in seconds. Reddy's comments highlight that this speed does not automatically satisfy security, compliance or performance requirements in live systems. Organisations still need code review processes, architectural governance and clear standards for accepting or rejecting AI-generated output.

His view also reflects a growing pattern where experienced engineers supervise AI-generated work at scale. Junior developers may rely on AI tools for implementation, while senior staff concentrate on system design, risk assessment and integration decisions.

AI for custom UIs

Reddy expects generative AI to move deeper into application configuration and user experience in the next phase of adoption. He pointed to pressure on business software vendors to shorten customisation cycles.

"In 2026 and beyond, we may see generative AI powering UI customization for a broad set of use cases, such as ERP, CRM, Fintech or Banking apps. Instead of customizing the apps using a dev team, application vendors may lean towards generative AI because of the pressures that come from the market. The time it takes to customize use-cases has been shrinking; businesses want to go live quickly, it's a race that they need to win," said Reddy.

Enterprise resource planning, customer relationship management and financial platforms often require significant tailoring for each customer. Vendors maintain configuration frameworks and industry-specific templates. Generative models trained on domain patterns could draft variants of user interfaces and workflow configurations more quickly. Human implementers would still validate regulatory compliance and usability.

This predicted shift aligns with broader moves towards "config over code" in business applications. Vendors seek ways to reduce the number of bespoke development projects while still meeting demands for differentiation and local fit. Generative AI may become an intermediate layer between generic product features and customer-specific implementations.

AI agents and long tail

Reddy also expects product organisations to adopt AI agents that target numerous narrow problems rather than a small number of broad use cases. He framed this as a change in how software companies think about value creation.

"In the future, product companies and teams that can use AI agents across the long tail of use cases are going to be more valuable. The use of generative AI in building AI agents that solve a specific problem very well, and solve the long tail of use cases, is going to be a key driver of what we are to see next."

AI agents in this context act on behalf of users within applications. They can execute multi-step tasks, call APIs and respond to real-time inputs. Long-tail use cases often include niche workflows that would not justify dedicated development in traditional models. Generative AI lowers the cost of building and adapting these task-specific agents over time.

Reddy's comments point towards a product strategy in which vendors assemble portfolios of specialised AI agents inside larger platforms. Each agent handles a clearly defined task, such as reconciling a subset of financial records or preparing a particular type of customer communication. A large number of such agents could address edge cases that have remained manual.

The combination of faster code generation, AI-assisted UI configuration and specialised agents may change how software businesses staff teams and price products. Senior engineers could spend more time curating AI outputs and designing reusable patterns. Product managers may focus on mapping long-tail workflows that AI agents can take over in stages.

Reddy's prediction that teams who exploit these agents across many such niches gain advantage reflects a wider view in the software industry that value will accrue to those who convert general-purpose AI models into tightly scoped, reliable workflows at scale.